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Floodplain Mapping through Support Vector Machine and Optical/Infrared Images from Landsat 8 OLI/TIRS Sensors: Case Study from Varanasi

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Abstract

Floods are among the most destructive natural disasters causing huge loss to life and property. Any flood management strategy requires floodplain mapping through discrimination of the flood prone areas. The city of Varanasi or Benaras is believed to be the oldest continuously inhabited city of the world. This study aims to develop tools for mapping and discrimination of floodplain of river Ganga at Varanasi. During 2014 floods, the flooded areas were extracted through Normalized Difference Water Index (NDWI) and by Modified NDWI (MNDWI) using the NIR and SWIR bands separately from that of the Landsat 8 satellite imagery. The inundated areas were then identified through Support Vector Machines (SVMs) classification. The results reveal that the MNDWI images provide a better result for flood discrimination than the NDWI images. Ground based measurements for floodplain distance varied between 11 ± 5 m at Janki ghat (bank) and 80 ± 5 m at Asi ghat. The validation between measured and SVMs derived values indicate a strong positive correlation of 0.88 and a low value of Root Mean Square Error (RMSE) of 12.62. The t-test is suggestive of no significant difference between the observed and SVMs values at 95% confidence level, indicating a satisfactory performance of the SVMs for floodplain mapping using Landsat 8 imagery. Therefore, the methodology proposed in this study provides a novel and robust way for floodplain mapping and has potential applications in disaster management and mitigation in the flood affected regions.

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References

  • Bellos V (2012) Ways for flood hazard mapping in urbanized environments: a short literature review. Water Utility J 4:25–31

    Google Scholar 

  • Bhavsar P (1984) Review of remote sensing applications in hydrology and water resources management in India. Adv Space Res 4:193–200

    Article  Google Scholar 

  • Cabanillas D, Comas J, Llorens L, Poch M, Ceccaroni L, Willmott S (2004) Implementation of the STREAMES environmental decision-support system. In: Complexity and Integrated Resources Management, Transactions of the 2nd Biennial Meeting of the International Environmental Modelling and Software Society, IEMSs, Osnabruck, Germany

  • Ceccaroni L (2000) Integration of a rule-based expert system, a case-based reasoner and an ontological knowledge-base in the wastewater domain. In: Proceedings of ECAI 2000-W07: Binding Environmental Sciences and Artificial Intelligence (BESAI2000), pp 8.1–8.10

  • Das DB, Thirakulchaya T, Deka L, Hanspal NS (2015) Artificial neural network to determine dynamic effect in capillary pressure relationship for two-phase flow in porous media with micro-heterogeneities. Environ Process 2:1–18

    Article  Google Scholar 

  • Fernandes J, Leal J, Cardoso A (2012) Flow structure in a compound channel with smooth and rough floodplains. Eur Water 38:3–12

    Google Scholar 

  • Freudenberger L, Hobson PR, Schluck M, Ibisch PL (2012) A global map of the functionality of terrestrial ecosystems. Ecol Complexity 12:13–22

    Article  Google Scholar 

  • Grimm NB, Faeth SH, Golubiewski NE, Redman CL, Wu J, Bai X, Briggs JM (2008) Global change and the ecology of cities. Science 319:756–760

    Article  Google Scholar 

  • Hobson P, Ibisch P (2010) An alternative conceptual framework for sustainability: systemics and thermodynamics. In: Interdependence of biodiversity and development under global change. Secretariat of the Convention on Biological Diversity, Montreal, pp 126–147

    Google Scholar 

  • Huang X, Tan H, Zhou J, Yang T et al (2008) Flood hazard in Hunan province of China: an economic loss analysis. Nat Hazards 47:65–73

    Article  Google Scholar 

  • IPCC (2012) Managing the risks of extreme events and disasters to advance climate change adaptation. In: Field CB, Barros V, Stocker TF, Qin D, Dokken DJ, Ebi KL, Mastrandrea MD, Mach KJ, Plattner G-K, Allen SK, Tignor M, Midgley PM (eds) A special report of working groups I and II of the intergovernmental panel on climate change. Cambridge, UK, Cambridge University Press, pp 582

  • Islam T, Rico-Ramirez MA, Han D, Srivastava PK (2012) Artificial intelligence techniques for clutter identification with polarimetric radar signatures. Atmos Res 109:95–113

    Article  Google Scholar 

  • Ji L, Zhang L, Wylie B (2009) Analysis of dynamic thresholds for the normalized difference water index. Photogrammetric Eng Remote Sens 75:1307–1317

    Article  Google Scholar 

  • JNNURM (2006) City development plan for Varanasi. Municipal Corporation, Varanasi

    Google Scholar 

  • Kavzoglu T, Colkesen I (2009) A kernel functions analysis for support vector machines for land cover classification. I J of Appl Earth Observation and Geoinformation 11:352–359

    Article  Google Scholar 

  • Keuchel J, Naumann S, Heiler M, Siegmund A (2003) Automatic land cover analysis for Tenerife by supervised classification using remotely sensed data. Remote Sens Environ 86:530–541

    Article  Google Scholar 

  • Kiedrzyńska E, Kiedrzyński M, Zalewski M (2015) Sustainable floodplain management for flood prevention and water quality improvement. Nat Hazards 76:955–977

    Article  Google Scholar 

  • Kundu S, Aggarwal S, Kingma N, Mondal A, Khare D (2015) Flood monitoring using microwave remote sensing in a part of Nuna river basin, Odisha, India. Nat Hazards 76:123–138

    Article  Google Scholar 

  • Latt ZZ (2015) Application of feed forward artificial neural network in Muskingum flood routing: a black-box forecasting approach for a natural river system. Water Resour Manag 29:4995–5014

    Article  Google Scholar 

  • Lavell A (2009) Technical study in integrating climate change adaptation and disaster risk management in development planning and policy. Study undertaken for the Inter-American Development Bank. Washington, DC

    Google Scholar 

  • Li D-C, Liu C-W (2010) A class possibility based kernel to increase classification accuracy for small data sets using support vector machines. Expert Systems Appl 37:3104–3110

    Article  Google Scholar 

  • McFeeters S (1996) The use of the normalized difference water index (NDWI) in the delineation of open water features. I J Remote Sens 17:1425–1432

    Article  Google Scholar 

  • Mitsch WJ, Zhang L, Fink DF, Hernandez ME, Altor AE, Tuttle CL, Nahlik AM (2008) Ecological engineering of floodplains. Ecohydrol Hydrobiol 8:139–147

    Article  Google Scholar 

  • Nandi I, Tewari A, Shah K (2016) Evolving human dimensions and the need for continuous health assessment of Indian rivers. Curr Sci 111:263–271. doi:10.18520/cs/v111/i2/263-271

    Article  Google Scholar 

  • Ouma YO, Tateishi R (2006) A water index for rapid mapping of shoreline changes of five east African rift valley lakes: an empirical analysis using Landsat TM and ETM+ data. I J Remote Sens 27:3153–3181

    Article  Google Scholar 

  • Pal M, Foody GM (2010) Feature selection for classification of hyperspectral data by SVM. IEEE Transactions on Geosci Remote Sens 48:2297–2307

    Article  Google Scholar 

  • Pal M, Mather PM (2004) Assessment of the effectiveness of support vector machines for hyperspectral data. Future Gen Comput Sys 20:1215–1225

    Article  Google Scholar 

  • Patel DP, Srivastava PK (2013) Flood hazards mitigation analysis using remote sensing and GIS: correspondence with town planning scheme. Water Resour Manag:1–16

  • Patel DP, Srivastava PK (2014) Application of geo-spatial technique for flood inundation mapping of low lying areas. In: Remote Sensing Applications in Environmental Research. Society of Earth Scientists Series. Springer International Publishing, pp 113–130. doi:10.1007/978-3-319-05906-8_7

  • Petropoulos GP, Kontoes C, Keramitsoglou I (2011) Burnt area delineation from a uni-temporal perspective based on Landsat TM imagery classification using support vector machines. I J Appl Earth Observation Geoinformation 13:70–80

    Article  Google Scholar 

  • Pradhan B (2013) A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Comput Geosci 51:350–365

    Article  Google Scholar 

  • Raclot D (2006) Remote sensing of water levels on floodplains: a spatial approach guided by hydraulic functioning. I J Remote Sens 27:2553–2574

    Article  Google Scholar 

  • Raghavendra N, Deka S, Chandra P (2014) Support vector machine applications in the field of hydrology: a review. Appl Soft Comput 19:372–386. doi:10.1016/j.asoc.2014.02.002

    Article  Google Scholar 

  • Ramana RV, Krishna B, Kumar S, Pandey N (2013) Monthly rainfall prediction using wavelet neural network analysis. Water Resour Manag 27:3697–3711

    Article  Google Scholar 

  • Sanyal J, Lu X (2004) Application of remote sensing in flood management with special reference to monsoon Asia: a review. Nat Hazards 33:283–301

    Article  Google Scholar 

  • Schowengerdt RA (1980) Reconstruction of multispatial, multispectral image data using spatial frequency content. Photogrammetric Eng Remote Sens 46:1325–1334

    Google Scholar 

  • Schroeder TA, Cohen WB, Song C, Canty MJ, Yang Z (2006) Radiometric correction of multi-temporal Landsat data for characterization of early successional forest patterns in western Oregon. Remote Sens Environ 103:16–26

    Article  Google Scholar 

  • Shah K, Nandi I, Singh N (2014a) Towards water security through sustainable management of water resources. Curr Sci 106:786–904

  • Shah K, Sharma PK, Nandi I, Singh N (2014b) Water sustainability: reforming water management in new global era of climate change. Environ Sci Pollu Res 21(19):11603–11604

  • Singh A (1989) Review article digital change detection techniques using remotely-sensed data. I J Remote Sens 10:989–1003

    Article  Google Scholar 

  • Singh RP (1993) Banaras (Varanasi) cosmic order, Sacred City. Hindu Traditions, Tara Book Agency, Varanasi

    Google Scholar 

  • Singh SK, Srivastava PK, Gupta M, Thakur JK, Mukherjee S (2014) Appraisal of land use/land cover of mangrove forest ecosystem using support vector machine. Environ Earth Sci 71:2245–2255

    Article  Google Scholar 

  • Smith LC (1997) Satellite remote sensing of river inundation area, stage, and discharge: a review. Hydrol Process 11:1427–1439

    Article  Google Scholar 

  • Srivastava PK, Han D, Rico-Ramirez MA, Bray M, Islam T (2012) Selection of classification techniques for land use/land cover change investigation. Adv Space Res 50:1250–1265

    Article  Google Scholar 

  • Srivastava PK, Han D, Rico-Ramirez MA, Al-Shrafany D, Islam T (2013) Data fusion techniques for improving soil moisture deficit using SMOS satellite and WRF-NOAH land surface model. Water Resour Manag 27:5069–5087

    Article  Google Scholar 

  • Srivastava PK, Yaduvanshi A, Singh SK, Islam T, Gupta M (2016) Support vector machine and generalized linear model for quantifying soil dehydrogenase activity in agro-forestry system of mid altitude central Himalaya. Environ Earth Sci. doi:10.1007/s12665–015–5074-3

    Google Scholar 

  • Syvitski JP, Overeem I, Brakenridge GR, Hannon M (2012) Floods, floodplains, delta plains—a satellite imaging approach. Sediment Geol 267:1–14

    Article  Google Scholar 

  • Tehrany MS, Pradhan B, Jebur MN (2014) Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS. J Hydrol 512:332–343. doi:10.1016/j.jhydrol.2014.03.008

    Article  Google Scholar 

  • Vapnik VN (2000) The nature of statistical learning theory, ser. Statistics for engineering and information science. New York: Springer 21:1003–1008

    Google Scholar 

  • Vapnik VN, Chervonenkis AY (1971) Theory of uniform convergence of relative frequencies of events to their probabilities and problems of search for an optimal solution from empirical data. Automation Remote Control 32:207–217

    Google Scholar 

  • Varoonchotikul P (2003) Flood forecasting using artificial neural networks. In: Balkema Pubhshers AA (ed) A member of Swets & Zeithnger Publishers. The Netherlands

  • Volpi M, Petropoulos GP, Kanevski M (2013) Flooding extent cartography with Landsat TM imagery and regularized kernel Fisher’s discriminant analysis. Comput Geosci 57:24–31

    Article  Google Scholar 

  • Xu H (2006) Modification of normalized difference water index (NDWI) to enhance open water features in remotely sensed imagery. I J Remote Sens 27:3025–3033

    Article  Google Scholar 

  • Zalewski M (2014a) Ecohydrology and hydrologic engineering: regulation of hydrology-biota interactions for sustainability. J Hydrol Eng 20:A4014012

    Article  Google Scholar 

  • Zalewski M (2014b) Ecohydrology, biotechnology and engineering for cost efficiency in reaching the sustainability of biogeosphere. Ecohydrol Hydrobiol14:14–20

  • Zhang Z, Lu W, Zhao Y, Song W (2014) Development tendency analysis and evaluation of the water ecological carrying capacity in the Siping area of Jilin province in China based on system dynamics and analytic hierarchy process. Ecol Model 275:9–21

    Article  Google Scholar 

  • Zhu G, Blumberg DG (2002) Classification using ASTER data and SVM algorithms: the case study of beer Sheva, Israel. Remote Sens Environ 80:233–240

    Article  Google Scholar 

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Acknowledgements

Authors are grateful to the University Grant Commission, Government of India for providing the fellowship to the first author. Authors would like to thanks METI and NASA for providing ASTER GDEM as well as USGS for providing Landsat 8 OLI/TIRS images.

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  1. Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi, 221005, India

    Ipsita Nandi, Prashant K. Srivastava & Kavita Shah

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  1. Ipsita Nandi
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  2. Prashant K. Srivastava
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  3. Kavita Shah
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Correspondence to Kavita Shah.

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Nandi, I., Srivastava, P.K. & Shah, K. Floodplain Mapping through Support Vector Machine and Optical/Infrared Images from Landsat 8 OLI/TIRS Sensors: Case Study from Varanasi. Water Resour Manage 31, 1157–1171 (2017). https://doi.org/10.1007/s11269-017-1568-y

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